# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("JJinBBangMan/distilbert-base-uncased-finetuned-imdb")
model = AutoModelForMaskedLM.from_pretrained("JJinBBangMan/distilbert-base-uncased-finetuned-imdb")Quick Links
distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 2.4142
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.7015 | 1.0 | 157 | 2.4981 |
| 2.5816 | 2.0 | 314 | 2.4282 |
| 2.5366 | 3.0 | 471 | 2.4515 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Model tree for JJinBBangMan/distilbert-base-uncased-finetuned-imdb
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JJinBBangMan/distilbert-base-uncased-finetuned-imdb")